Automated software engineering, particularly resolving real-world issues on benchmarks like SWE-bench, remains a significant challenge for Large Language Models (LLMs). To address this, we introduce SWE-Swiss, a two-phase training recipe that systematically develops these capabilities. Our approach first decomposes issue resolution into three core skills: Localization, Repair, and Unit Test Generation. In the first phase, we perform multi-task Supervised Fine-Tuning (SFT) on three new, meticulously curated datasets to build a versatile foundation. The second phase applies targeted Reinforcement Learning (RL), using direct feedback from test execution to boost the critical skill of code repair. The resulting model, SWE-Swiss-32B, establishes a new state-of-the-art for open-source models in its size class, achieving a 60.2% score on the SWE-bench Verified benchmark and placing it in the same top-tier performance bracket as much larger models. Finally, we show that despite its specialized training, SWE-Swiss-32B demonstrates strong generalization to other common LLM benchmarks. To accelerate research in the community, we are open-sourcing the models and our complete training datasets.

We introduce Retrieval-Based Speculative Decoding (REST), a novel algorithm designed to speed up language model generation. The key insight driving the development of REST is the observation that the process of text generation often includes certain common phases and patterns. Unlike previous methods that rely on a draft language model for speculative decoding, REST harnesses the power of retrieval to generate draft tokens. This method draws from the reservoir of existing knowledge, retrieving and employing relevant tokens based on the current context. Its plug-and-play nature allows for seamless integration and acceleration of any language model, all without necessitating additional training. When benchmarked on 7B and 13B language models in a single-batch setting, REST achieves a significant speedup of 1.62 × to 2.36 × on code or text generation. The source code of REST is available at https://github.com/FasterDecoding/REST.